A study of the pyjamas purchasing behaviour of Chinese consumers in Hangzhou, China
Bibliographic record
Abstract
Purpose This study aims to explore and understand consumers' perceptions and behaviour towards pyjamas in the People's Republic of China (China). Design/methodology/approach A quantitative analysis and comparative methods were used for this study. From a large body of literature, seven product attributes were identified and used to measure and evaluate what constitutes consumers' purchasing decision for pyjamas. A total of 203 usable surveys were compiled, analyzed and collated. Findings This study shows evidence that consumers are more conscious of the functional values of a low‐involvement product than the symbolic values. The results of this survey indicate that comfort, fabric and quality are significant attributes, whereas country‐of‐origin and brand are relatively insignificant determinants for purchasing a pair of pyjamas. Research limitations/implications Limitations of this study include the use of a convenience sample of female college students and confinement to a specific product – pyjamas. The results of this study are useful for fashion designers and marketers to understand Chinese consumers' perceptions of pyjamas. Originality/value This study is one of the few consumer research studies on a low‐involvement and privately consumed apparel product – pyjamas. The findings of this study provide insight and implications for fashion practitioners to develop their product and business in China.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".